IEEE

On the Convergence of (Stochastic) Gradient Descent for Kolmogorov–Arnold Networks

Abstract: Kolmogorov–Arnold Networks (KANs), a recently proposed neural network architecture, have gained significant attention in the deep learning community, due to their potential as a viable ...
IEEE

Survey of Gradient Descent Variants and Evaluation Criteria

Abstract: Gradient descent is a fundamental optimization algorithm widely used in artificial intelligence to minimize the loss function and find the optimal parameters of a model, so optimize the ...
Visual Studio Magazine

Kernel Ridge Regression with Stochastic Gradient Descent Training Using JavaScript

Dr. James McCaffrey presents a complete end-to-end demonstration of the kernel ridge regression technique to predict a single numeric value. The demo uses stochastic gradient descent, one of two ...
Frontiers

Sliding down the socioeconomic health gradient of COVID-19 in New York City: multinomial regression analyses of disproportionate financial hardship for Black, Latino, and Asian ...

Background: Distinct socioeconomic gradients in COVID-19 outcomes were observed across the United States, so an evaluation of individual resident characteristics related to economic deprivation (race ...
GitHub

Vision-Language Gradient Descent-driven All-in-One Deep Unfolding Networks

Abstract: Dynamic image degradations, including noise, blur and lighting inconsistencies, pose significant challenges in image restoration, often due to sensor limitations or adverse environmental ...